Nigro Carlos Alberto, González Sergio, Arce Anabella, Aragone María Rosario, Nigro Luciana
Sleep Laboratory, Hospital Alemán, Pedro Goyena 620 3 B, CP 1424, Buenos Aires, Argentina,
Sleep Breath. 2015 May;19(2):569-78. doi: 10.1007/s11325-014-1048-z. Epub 2014 Aug 13.
Patients under treatment with continuous positive airway pressure (CPAP) may have residual sleep apnea (RSA).
The main objective of our study was to evaluate a novel auto-CPAP for the diagnosis of RSA.
All patients referred to the sleep laboratory to undergo CPAP polysomnography were evaluated. Patients treated with oxygen or noninvasive ventilation and split-night polysomnography (PSG), PSG with artifacts, or total sleep time less than 180 min were excluded. The PSG was manually analyzed before generating the automatic report from auto-CPAP. PSG variables (respiratory disturbance index (RDI), obstructive apnea index, hypopnea index, and central apnea index) were compared with their counterparts from auto-CPAP through Bland-Altman plots and intraclass correlation coefficient. The diagnostic accuracy of autoscoring from auto-CPAP using different cutoff points of RDI (≥5 and 10) was evaluated by the receiver operating characteristics (ROCs) curve.
The study included 114 patients (24 women; mean age and BMI, 59 years old and 33 kg/m(2); RDI and apnea/hypopnea index (AHI)-auto median, 5 and 2, respectively). The average difference between the AHI-auto and the RDI was -3.5 ± 3.9. The intraclass correlation coefficient (ICC) between the total number of central apneas, obstructive, and hypopneas between the PSG and the auto-CPAP were 0.69, 0.16, and 0.15, respectively. An AHI-auto >2 (RDI ≥ 5) or >4 (RDI ≥ 10) had an area under the ROC curve, sensitivity, specificity, positive likelihood ratio, and negative for diagnosis of residual sleep apnea of 0.84/0.89, 84/81%, 82/91%, 4.5/9.5, and 0.22/0.2, respectively.
The automatic analysis from auto-CPAP (S9 Autoset) showed a good diagnostic accuracy to identify residual sleep apnea. The absolute agreement between PSG and auto-CPAP to classify the respiratory events correctly varied from very low (obstructive apneas, hypopneas) to moderate (central apneas).
接受持续气道正压通气(CPAP)治疗的患者可能存在残余睡眠呼吸暂停(RSA)。
我们研究的主要目的是评估一种新型自动CPAP用于RSA诊断的效果。
对所有转诊至睡眠实验室接受CPAP多导睡眠监测的患者进行评估。排除接受氧气或无创通气治疗以及进行分夜多导睡眠监测(PSG)、存在伪差的PSG或总睡眠时间少于180分钟的患者。在自动CPAP生成自动报告之前,对PSG进行人工分析。通过Bland-Altman图和组内相关系数,将PSG变量(呼吸紊乱指数(RDI)、阻塞性呼吸暂停指数、低通气指数和中枢性呼吸暂停指数)与其在自动CPAP中的对应变量进行比较。使用不同的RDI截断点(≥5和10)评估自动CPAP自动评分对残余睡眠呼吸暂停的诊断准确性,通过受试者操作特征(ROC)曲线进行分析。
该研究纳入了114例患者(24例女性;平均年龄和体重指数分别为59岁和33kg/m²;自动CPAP的RDI和呼吸暂停/低通气指数(AHI)中位数分别为5和2)。自动CPAP的AHI与RDI之间的平均差值为-3.5±3.9。PSG与自动CPAP之间中枢性呼吸暂停、阻塞性呼吸暂停和低通气总数的组内相关系数(ICC)分别为0.69、0.16和0.15。自动CPAP的AHI>2(RDI≥5)或>4(RDI≥10)时,诊断残余睡眠呼吸暂停的ROC曲线下面积、敏感性、特异性、阳性似然比和阴性似然比分别为0.84/0.89、84/81%、82/91%、4.5/9.5和0.22/0.2。
自动CPAP(S9 Autoset)的自动分析在识别残余睡眠呼吸暂停方面显示出良好的诊断准确性。PSG与自动CPAP在正确分类呼吸事件方面的绝对一致性从非常低(阻塞性呼吸暂停、低通气)到中等(中枢性呼吸暂停)不等。